Final Project - Mass Shooting Section

Maps

Centered latitude and longitude points for each state.

url = "https://inkplant.com/code/state-latitudes-longitudes"

coord_webpage = read_html(url)

cent_lat_and_long = 
    coord_webpage %>%
  html_nodes(css = "table") %>% 
     .[[1]] %>% 
  html_table(header = TRUE) %>% 
  as_tibble() %>% 
    janitor::clean_names() %>% 
    filter(state != "Alaska", state != "Hawaii")

Bubble map of mass shootings by state.

n_shooting_inc = 
    gun_v_tidy %>% 
    filter(mass_shooting == "Yes") %>% 
    group_by(state) %>% 
    summarize(n_incidents = n())

usa_shooting_inc =
    inner_join(n_shooting_inc, cent_lat_and_long, by = "state")

usa = map_data("state")

## 
## Attaching package: 'maps'

## The following object is masked from 'package:purrr':
## 
##     map

ggplot() +
    geom_path(data = usa, aes(x = long, y = lat, group = group)) +
    geom_point(data = usa_shooting_inc, aes(x = longitude, y = latitude, size = n_incidents), color = "darkseagreen3") +
    scale_size(range = c(0, 12))

Plotly interactive map of shootings by state.

gun_v_tidy %>%
    filter(state != "Alaska", state != "Hawaii", longitude < 0) %>% 
    mutate(n_affected_cat = ifelse(n_affected %in% 4:9, "2: 4-9",
                                                    ifelse(n_affected %in% 10:19, "3: 10-19",
                                                    ifelse(n_affected %in% 20:34, "4: 20-34",
                                                    ifelse(n_affected %in% 35:49, "5: 35-49",
                                                    ifelse(n_affected > 50, "6: 50+", "1: 1-3")))))) %>% 
    mutate(city_state = str_c(city_or_county, state, sep = ", ")) %>% 
    mutate(text_label = str_c(city_state, '\n', year,'\nKilled: ', n_killed, '\nInjured: ', n_injured)) %>% 
  plot_ly(x = ~longitude, y = ~latitude, type = "scatter", mode = "markers",
          alpha = 0.5, 
          color = ~n_affected_cat,
                colors = "Accent",
                text = ~text_label)

Time trends

n_yearly_shootings = 
    gun_v_tidy %>% 
    group_by(year) %>% 
    summarize(count = n()) %>% 
    filter(year != 2013, year != 2018) %>% 
    mutate(year = as.numeric(year))

n_yearly_mass_violence = 
    gun_v_tidy %>% 
    filter(mass_shooting == "Yes") %>% 
    group_by(year) %>% 
    summarize(count = n()) %>% 
    filter(year != 2013, year != 2018) %>% 
    mutate(year = as.numeric(year))
    
shootings = 
    n_yearly_shootings %>% 
  ggplot(aes(x = year, y = count, fill = year)) +
  geom_bar(stat = "identity") +  
    labs(
    title = "Cumulative Number of Gun Violence Incidents per Year",
    x = "Year",
    y = "Count"
  ) 

mass_violence =
    n_yearly_mass_violence %>% 
    ggplot(aes(x = year, y = count, fill = year)) +
  geom_bar(stat = "identity") +
    labs(
    title = "Cumulative Number of Mass Shootings per Year",
    x = "Year",
    y = "Count"
  ) 

shootings / mass_violence

Proportion of deaths in a given gun violence incidence.

prop_data = 
    gun_v_tidy %>% 
    mutate(prop_killed = n_killed / (n_killed + n_injured))

prop_data %>% 
    group_by(year) %>% 
    summarize(mean_prop = mean(prop_killed)) %>% 
    filter(year != 2013, year != 2018) %>% 
    ggplot(aes(x = year, y = mean_prop)) +
    geom_point()

prop_data %>% 
    group_by(state) %>% 
    summarize(mean_prop = mean(prop_killed)) %>% 
    mutate(state = forcats::fct_reorder(state, mean_prop)) %>% 
    ggplot(aes(x = state, y = mean_prop)) +
    geom_point() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 6))